Triple

T30210304
Position Surface form Disambiguated ID Type / Status
Subject Nez Cassé locomotives E768049 entity
Predicate hasSubclass P1244 FINISHED
Object ONCF DH 380 series
The ONCF DH 380 series is a class of diesel-electric locomotives used by Morocco’s national railway operator, derived from the French “Nez Cassé” family of locomotives.
E1928441 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: ONCF DH 380 series | Statement: [Nez Cassé locomotives, hasSubclass, ONCF DH 380 series]
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: ONCF DH 380 series
Triple: [Nez Cassé locomotives, hasSubclass, ONCF DH 380 series]
Generated description
The ONCF DH 380 series is a class of diesel-electric locomotives used by Morocco’s national railway operator, derived from the French “Nez Cassé” family of locomotives.

Provenance (5 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69f2247eb0848190b4032f302d39c0d9 completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_69f67ff0c0dc8190862a037439b36edf completed May 2, 2026, 10:51 p.m.
NED1 Entity disambiguation (via context triple) batch_6a2898ba07f8819095b22f735eac9b28 completed June 9, 2026, 10:50 p.m.
NEDg Description generation batch_6a289ab8b9c881908958b0824a1990c7 completed June 9, 2026, 10:59 p.m.
NED2 Entity disambiguation (via description) batch_6a289b39ce9c8190a342e61537c2f11f completed June 9, 2026, 11:01 p.m.
Created at: April 29, 2026, 7:32 p.m.